1. Field of the Invention
The present invention relates to control units for engine systems, in particular control units having a separate arithmetic unit for evaluating data-based function models, for example, Gaussian process models.
2. Description of the Related Art
Up to now, function models in control units, i.e., the control path and system models, have been implemented through the specification of characteristic maps, characteristic curves, or functions emulating a physical system. These models are adapted by a user by adjusting model parameters to the conditions of the physical system.
The use of non-parametric data-based function models constitutes an alternative, with which the functions of physical systems may be essentially emulated without parameter specifications. For example, a Gaussian process model, which is essentially defined using hyperparameters and nodes, may be used as a data-based function model. The data-based function models are created based on training data which may be ascertained in a testing system. The nodes for the Gaussian process model may correspond to the training data, may be selected from these data, or may be generated from the training data.
In particular, local effects may not be properly mapped by the created data-based function model under certain circumstances. If a data-based function model has already been determined based on training data of an initial training data record, it is difficult to take into consideration training data of a subsequently ascertained training data record in a proper manner in the data-based function model which has already been created. However, simply merging the training data records with or without variation of the hyperparameters of the data-based model allows local effects to be properly taken into consideration only if the training data of the subsequently added training data record have a sufficient number of measuring points. Thus, the additional measuring points may have a sufficient weight in relation to the measuring points of the training data of the initial training data record. Furthermore, it is required that the measuring points of the subsequently added training data record do not conflict with already existing measuring points of the initial training data record, i.e., have a relatively large deviation from them. Otherwise, data-based function models are obtained having high measurement noise and an accordingly high modeling error for the function values in the range of the local effect.
Control units having a microcontroller and a separate model calculation unit for calculating data-based models in a control unit are known from the related art. Thus, for example, a control unit having an additional logic circuit is known from Published German patent application document DE 10 2010 028 259 A1, which is designed for calculating exponential functions in order to support the execution of Bayesian regression methods, which are required in particular for the calculation of Gaussian process models.
Furthermore, an additional method for adding measuring points of an additional training data record to an existing Gaussian process model is known from C. Plagemann, K. Kersting, W. Burgard, “Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness,” ICML Proceedings, pp. 204-2116, 2006. However, this method is inefficient, in particular since the parameter optimization is difficult.